Analysis and prediction of indoor air pollutants in a subway station using a new key variable selection method

JungJin Lim; YongSu Kim; TaeSuk Oh; MinJung Kim; OnYu Kang; Jeong Tai Kim; In-Won Kim; Jo-Chun Kim; Jae-Sik Jeon; ChangKyoo Yoo

Korean Journal of Chemical Engineering, Vol.29, No.8, 994-1003, August, 2012

DOI10.1007/s11814-011-0278-z Export Citation

Analysis and prediction of indoor air pollutants in a subway station using a new key variable selection method

JungJin Lim¹, YongSu Kim¹, TaeSuk Oh¹, MinJung Kim¹, OnYu Kang¹, Jeong Tai Kim², In-Won Kim³, Jo-Chun Kim^{4, 5}, Jae-Sik Jeon⁶, and ChangKyoo Yoo^{1, †}

¹Department of Environmental Science and Engineering, Center for Environmental Studies, Kyung Hee University, Gyeonggi-do 446-701, Korea
²Department of Architectural Engineering, College of Engineering, Kyung Hee University, Gyeonggi-do 446-701, Korea
³Department of Chemical Engineering, Konkuk University, Seoul 143-701, Korea
⁴Department of Environmental Engineering, Konkuk University, Seoul 143-701, Korea
⁵Department of Advanced Technology Fusion, Konkuk University, Seoul 143-701, Korea
⁶Seoul Metropolitan Government Research Institute of Public Health and Environment, Yongmeori2-gil (Juam-dong1), Gwacheon-si, Gyeonggi-do 427-070, Korea

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A new key variable selection and prediction model of IAQ that can select key variables governing indoor air quality (IAQ), such as PM10, CO2, CO, VOCs and formaldehyde, are suggested in this paper. The essential problem of the prediction model is the question of which of the original variables are the most important for predicting IAQ. The next issue is determining the number of key variables that should be ranked. A new index of discriminant importance in the projection (DIP) of Fisher’s linear discriminant (FLD) is suggested for selecting key variables of the prediction models with multiple linear regression (MLR) and partial least squares (PLS), as well as for ranking the importance of input measurement variables on IAQ prediction. The prediction models were applied to a real IAQ dataset from telemonitoring data (TMS) in a metro system. The prediction results of the model using all variables were compared with the results of the model using only key variables of DIP. It shows that the use of our new variable selection method cannot only reduce computational effort, but will also enhance the prediction performances of the models.

Keywords:Indoor Air Quality (IAQ);Pollution Prediction;Fisher’s Linear Discriminant (FLD);Platform Screen Door (PSD) System;Variable Selection

[References]

Chiang LH, Russell E, Braatz RD, Chem. Int. Lab. Syst., 50, 243 (2000)
Duda RO, Hart PE, Stork DG, Pattern classification, 2nd Ed., John Wiley & Sons, New York (2001)
Furuya K, Kudo Y, Okinaga K, Yamukki M, Takahashi S, Araki Y, Hisamatsu Y, J. Trace and Microprobe Techniques., 19, 469 (2001)
Hastie T, Tibshirani R, Friedman J, The elements of statistical learning, Springer, USA (2009)
Kang SN, Hwang HJ, Park YM, Kim HK, Ro CU, Environ.Sci. Technol., 42, 9051 (2008)
Kim M, Rao AS, Yoo C, Ind. Eng. Chem. Res., 48(13), 6363 (2009)
Kim MJ, Kim MH, Kim YS, Yoo CK, Multivariate interpretation on the parameter correlation for over-determined system modeling of ASM model, 10th IWA Conference on Instrumentation Control and Automation (2009)
Kim NJ, Lee SS, Jeon JS, Kim JH, Kim MY, Evaluation of Factors to Affect PM-10 Concentration in Subway Station, Proceedings of Korean Society for Atmospheric Environment, 571 (2006)
Kim YS, Kim JT, Kim IW, Kim JC, Yoo CK, Environ.Eng. Sci., 27, 721 (2010)
Kim YS, Kim MH, Yoo CK, J. Hazard. Mater., 183(1-3), 441 (2010)
Kim Y, Kim M, Lim J, Kim JT, Yoo C, J. Hazard. Mater., 183(1-3), 448 (2010)
King JR, Jackson DA, Environmetrics., 10, 67 (1999)
Kourti T, Anal. Bioanal. Chem., 384, 1043 (2006)
Kwag HK, Jin KW, Kim W, Yang WS, Choi SJ, Park DU, Kor. J. Env. Hlth., 31, 379 (2005)
Liu SS, Liu HL, Yin CS, Wang LS, J. Chem. Inf. Comput.Sci., 43, 964 (2003)
Macgregor JF, Jaeckle C, Kiparissides C, Koutoudi M, AIChE J., 40(5), 826 (1994)
Murruni LG, Solanes V, Debray M, Kreiner AJ, Davidson J, Davidson M, Atoms. Environ., 43, 4577 (2009)
Nieuwenhuijsen MJ, Gomez-Perales JE, Colvile RN, Atmos.Environ., 41, 7995 (2007)
Paivi A, Tarja YT, Anu K, Timo M, Anne H, Kaarle H, Mika RI, Risto H, Tarja K, Matti J, Atmos. Environ., 39, 5059 (2005)
Ramadan Z, Song XH, Hopke PK, Johnson MJ, Scow KM, Anal. Chim. Acta., 446, 233 (2001)
Rigol AM, Camps M, Juan AD, Rauret G, Vidal M, Environ.Sci. Techonol., 42, 4029 (2008)
Yamamoto T, Shimameguri A, Ogawa M, Hashimoto I, Kano V, Application of Statistical Process Monitoring with External Analysis to an Industrial Monomer Plant, IFAC Symposium on Advanced Control of Chemical Processes, 405 (2004)

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[2] Duda RO, Hart PE, Stork DG, Pattern classification, 2nd Ed., John Wiley & Sons, New York (2001)

[3] Furuya K, Kudo Y, Okinaga K, Yamukki M, Takahashi S, Araki Y, Hisamatsu Y, J. Trace and Microprobe Techniques., 19, 469 (2001)

[4] Hastie T, Tibshirani R, Friedman J, The elements of statistical learning, Springer, USA (2009)

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[8] Kim NJ, Lee SS, Jeon JS, Kim JH, Kim MY, Evaluation of Factors to Affect PM-10 Concentration in Subway Station, Proceedings of Korean Society for Atmospheric Environment, 571 (2006)

[9] Kim YS, Kim JT, Kim IW, Kim JC, Yoo CK, Environ.Eng. Sci., 27, 721 (2010)

[10] Kim YS, Kim MH, Yoo CK, J. Hazard. Mater., 183(1-3), 441 (2010)

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[12] King JR, Jackson DA, Environmetrics., 10, 67 (1999)

[13] Kourti T, Anal. Bioanal. Chem., 384, 1043 (2006)

[14] Kwag HK, Jin KW, Kim W, Yang WS, Choi SJ, Park DU, Kor. J. Env. Hlth., 31, 379 (2005)

[15] Liu SS, Liu HL, Yin CS, Wang LS, J. Chem. Inf. Comput.Sci., 43, 964 (2003)

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[18] Nieuwenhuijsen MJ, Gomez-Perales JE, Colvile RN, Atmos.Environ., 41, 7995 (2007)

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